Setup
library(tidyverse)
Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
[30m-- [1mAttaching packages[22m --------------------------------------- tidyverse 1.2.1 --[39m
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library(lubridate)
Vedh攼㸶fter pakke: 㤼㸱lubridate㤼㸲
Det f昼㸸lgende objekt er maskeret fra 㤼㸱package:base㤼㸲:
date
library(readxl)
library(data.table)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
data.table 1.12.6 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com
Vedh攼㸶fter pakke: 㤼㸱data.table㤼㸲
De f昼㸸lgende objekter er maskerede fra 㤼㸱package:lubridate㤼㸲:
hour, isoweek, mday, minute, month, quarter, second, wday, week, yday, year
De f昼㸸lgende objekter er maskerede fra 㤼㸱package:dplyr㤼㸲:
between, first, last
Det f昼㸸lgende objekt er maskeret fra 㤼㸱package:purrr㤼㸲:
transpose
library(tsibble)
Vedh攼㸶fter pakke: 㤼㸱tsibble㤼㸲
Det f昼㸸lgende objekt er maskeret fra 㤼㸱package:data.table㤼㸲:
key
De f昼㸸lgende objekter er maskerede fra 㤼㸱package:lubridate㤼㸲:
interval, new_interval
Det f昼㸸lgende objekt er maskeret fra 㤼㸱package:dplyr㤼㸲:
id
library(feasts)
Indl攼㸶ser kr攼㸶vet pakke: fabletools
library(tsibble)
library(feasts)
Walmart
wm_train <- fread("~/kaggle/walmart/train.csv") %>%
janitor::clean_names() %>%
mutate(date = ymd(date)) %>%
mutate_at(vars(store, dept), as.factor)
wm_test<- fread("~/kaggle/walmart/test.csv") %>%
janitor::clean_names() %>%
mutate(date = ymd(date)) %>%
mutate_at(vars(store, dept), as.factor)
feats <- fread("~/kaggle/walmart/features.csv") %>% janitor::clean_names() %>% mutate(date = ymd(date))
stores <- fread("~/kaggle/walmart/stores.csv") %>% janitor::clean_names() %>% mutate(store = as.factor(store))


_
tsib %>%
mutate(is_holiday = as.numeric(is_holiday)) %>%
as.tibble() %>%
group_by(store, dept) %>%
summarize(
cor = cor(weekly_sales, is_holiday, method = "spearman", use = "na.or.complete")
) %>%
ungroup %>%
mutate(mean_cor = mean(abs(cor), na.rm = TRUE)) %>%
ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_cor))
train_w_var <- tsib %>%
ungroup %>%
mutate(store = as.integer(store)) %>%
left_join(feats %>% select(-is_holiday) %>% mutate(date = yearweek(date)), by = c("store", "date"))
get_correlations <- function(df) {
df %>%
corrr::correlate(method = "spearman") %>%
select(variable = rowname, cor = weekly_sales) %>%
filter(variable != "weekly_sales") %>%
as.tibble()
}
library(furrr)
plan(multiprocess)
cor_df <- train_w_var %>%
mutate_at(vars(-date, -store, -dept), replace_na, replace = 0) %>%
as.tibble() %>%
group_by(store, dept) %>%
select(-date) %>%
nest() %>%
mutate(cor = future_map(data, get_correlations, .progress = TRUE))
cor_df %>%
unnest(cor) %>%
group_by(variable) %>%
mutate(mean_val = mean(abs(cor), na.rm = TRUE)) %>%
ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_val)) + facet_grid(~variable)
train_w_var %>%
ggplot(aes(x = mark_down1, y = weekly_sales)) + geom_point() + geom_smooth()
overall_cor <- train_w_var %>%
mutate_at(vars(-date, -store, -dept), replace_na, replace = 0) %>%
as.tibble() %>%
select(-date, -store, -dept) %>%
nest() %>%
mutate(cor = future_map(data, get_correlations, .progress = TRUE))
overall_cor %>% unnest(cor) %>% select(-data) %>% arrange(desc(abs(cor)))
rm(cor_df, feats, overall_cor,stores, train_w_var, tsib, wm_feats, wm_test, wm_train)


ex%>%
left_join(svd_var_exp_df$data[[5]] %>% mutate(store = as.integer(store))) %>%
mutate(weekly_sales = replace_na(weekly_sales, 0)) %>%
mutate(error = weekly_sales - rec) %>%
group_by(comp) %>%
summarize(
sd_error = sd(error),
sd_sales = sd(weekly_sales),
pct_error = sd_error/sd_sales,
var_exp = 1 - pct_error
)
Joining, by = c("date", "is_holiday", "store")
#Rossman
rs_tsib <- rs_trn %>%
as_tsibble(key = store, index = date)
rs_feats <- rs_tsib %>%
features(sales, list(feat_stl, feat_spectral))
rs_feats %>%
gather(key = variable, value = value, seasonal_strength_week, trend_strength, spectral_entropy) %>%
ggplot(aes(x = value)) + geom_histogram() + facet_wrap(~variable)
rs_feats %>%
mutate(ds = "Rossmann") %>%
select(-store) %>%
mutate(seas_strength = seasonal_strength_week) %>%
# bind_rows(
# wm_feats %>% mutate(ds = "Walmart") %>% select(-store) %>% mutate(seas_strength = seasonal_strength_year)
# ) %>%
gather(key = variable, value = value, seas_strength, trend_strength, spectral_entropy) %>%
ggplot(aes(x = value, fill = ds)) + geom_histogram() + facet_wrap(ds~variable)
rs_w_feat <- rs_trn %>%
left_join(rs_states) %>%
left_join(weather, by = c("state_name" = "file", "date"))
rs_cor_df <- rs_w_feat %>%
select(-day_of_week, -state_name, -state, -max_gust_speed_km_h) %>%
as.tibble() %>%
group_by(store) %>%
mutate_if(is.character, ~as.numeric(as.factor(.x))) %>%
select(-date, weekly_sales = sales) %>%
nest() %>%
mutate(cor = future_map(data, get_correlations, .progress = TRUE, ))
rs_cor_df %>%
unnest(cor) %>%
group_by(variable) %>%
mutate(mean_val = mean(abs(cor), na.rm = TRUE)) %>%
ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_val)) + facet_wrap(~variable)
overall_cor <- rs_w_feat %>%
select(-day_of_week, -state_name, -state, -max_gust_speed_km_h) %>%
as.tibble() %>%
mutate_if(is.character, ~as.numeric(as.factor(.x))) %>%
select(-date, weekly_sales = sales) %>%
get_correlations()
overall_cor %>% arrange(desc(abs(cor)))
pcs <- rs_feats %>%
select(-store) %>%
mutate_all(~replace_na(.x, replace = mean(.x, na.rm = TRUE))) %>%
prcomp(scale=TRUE)
autoplot(pcs, loadings = TRUE, loadings.label = TRUE)
rm(rs_trn, rs_tsib, rs_w_feat, weather, rs_store, rs_states, rs_feats, rs_cor_df)
Grupo Bimbo
tictoc::tic()
bb_fact <- bb_trn[, lapply(.SD, as.factor), .SDcols = c("agencia_id", "canal_id", "ruta_sak", "cliente_id", "producto_id")]
tictoc::toc()
14.53 sec elapsed






obs <- agg[, .(cliente_id, producto_id, semana, exists = 1L)]
bt <- dcast(obs, cliente_id + producto_id ~ semana, value.var = "exists") %>%
melt(., id.vars = c("cliente_id", "producto_id"), measure.vars = c("3", "4", "5", "6", "7", "8", "9"), variable.name = "semana")
full_ds <- merge(bt[, semana := as.integer(semana)], agg, all.x = TRUE)
rm(bt, agg)
gc()
for (j in seq_len(ncol(full_ds))) {
set(full_ds, which(is.na(full_ds[[j]])),j,0)
}
rm(bb_fact, bb_test_fact)
full_ds <- full_ds[order(cliente_id, producto_id, semana)]
safe_spec <- possibly(feat_spectral, otherwise = NA)
full_ds[, .(entropy = safe_spec(demanda_uni_equil) %>% as.numeric), by = .(cliente_id, producto_id)]
plan(multiprocess)
full_ds_feat <- full_ds[, .(.(demanda_uni_equil)), by = .(cliente_id, producto_id)]
full_ds_feat[, ent := future_map(V1, ~safe_spec(.x) %>% as.numeric)]
---
title: "R Notebook"
output: html_notebook
---

# Setup
```{r}
library(tidyverse)
library(lubridate)
library(readxl)
library(data.table)
library(tsibble)
library(feasts)
```

# Walmart
```{r}
wm_train <- fread("~/kaggle/walmart/train.csv") %>%
  janitor::clean_names() %>%
  mutate(date = ymd(date)) %>%
  mutate_at(vars(store, dept), as.factor)

wm_test<- fread("~/kaggle/walmart/test.csv") %>%
  janitor::clean_names() %>%
  mutate(date = ymd(date)) %>%                
  mutate_at(vars(store, dept), as.factor)

feats <- fread("~/kaggle/walmart/features.csv") %>% janitor::clean_names() %>% mutate(date = ymd(date))
stores <- fread("~/kaggle/walmart/stores.csv") %>% janitor::clean_names() %>% mutate(store = as.factor(store))
```

```{r}
tsib <- wm_train %>%
  mutate(date = yearweek(date)) %>%
  as_tsibble(key = c("store", "dept"), index = date) %>%
  group_by_key(store, dept) %>%
  tsibble::fill_gaps(weekly_sales = 0)

tsib %>%
  as.tibble() %>% 
  group_by(store, dept) %>%
  summarize(n = n(),
         non_zero = sum(weekly_sales > 0),
         pct_non_zero = non_zero/n,
         num_seas = 52/n) %>%
  gather(key = measure, val = value, n, non_zero, pct_non_zero, num_seas) %>%
  ggplot(aes(x = value)) + geom_histogram() + facet_wrap(~measure, scales = "free") 

library(future)
plan(multiprocess)
plan(sequential)

tictoc::tic()
wm_feats <- tsib %>% 
  mutate(n = n()) %>%
  filter(n > 7) %>%
  features(weekly_sales, list(feat_stl, feat_spectral))
tictoc::toc()

pcs <- wm_feats %>%
  select(-store, -dept) %>%
  mutate_all(~replace_na(.x, replace = mean(.x, na.rm = TRUE))) %>%
  prcomp(scale=TRUE)


library(ggfortify)
autoplot(pcs, loadings = TRUE, loadings.label = TRUE)

wm_train %>%
  distinct(store, dept) %>%
  left_join(stores) %>%
  group_by(dept, type) %>%
  summarize(
    n_distinct(store),
    n = n()
  )
```


```{r}
wm_feats %>%
  gather(key = variable, value = value, seasonal_strength_year, trend_strength, spectral_entropy) %>%
  ggplot(aes(x = value)) + geom_histogram() + facet_wrap(~variable)
```


_
```{r}
tsib %>%
  mutate(is_holiday = as.numeric(is_holiday)) %>%
  as.tibble() %>%
  group_by(store, dept) %>%
  summarize(
    cor = cor(weekly_sales, is_holiday, method = "spearman", use = "na.or.complete")
  ) %>%
  ungroup %>%
  mutate(mean_cor = mean(abs(cor), na.rm = TRUE)) %>%
  ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_cor))
```

```{r}
train_w_var <- tsib %>%
  ungroup %>%
  mutate(store = as.integer(store)) %>%
  left_join(feats %>% select(-is_holiday) %>% mutate(date = yearweek(date)), by = c("store", "date"))
```

```{r}
get_correlations <- function(df) {
  df %>% 
    corrr::correlate(method = "spearman") %>%
    select(variable = rowname, cor = weekly_sales) %>%
    filter(variable != "weekly_sales") %>%
    as.tibble()
}

library(furrr)
plan(multiprocess)

cor_df <- train_w_var %>%
  mutate_at(vars(-date, -store, -dept), replace_na, replace = 0) %>%
  as.tibble() %>%
  group_by(store, dept) %>%
  select(-date) %>%
  nest() %>%
  mutate(cor = future_map(data, get_correlations, .progress = TRUE))
  
cor_df %>%
  unnest(cor) %>%
  group_by(variable) %>%
  mutate(mean_val = mean(abs(cor), na.rm = TRUE)) %>%
  ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_val)) + facet_grid(~variable)
```

```{r}
train_w_var %>%
  ggplot(aes(x = mark_down1, y = weekly_sales)) + geom_point() + geom_smooth()
```

```{r}
overall_cor <- train_w_var %>%
  mutate_at(vars(-date, -store, -dept), replace_na, replace = 0) %>%
  as.tibble() %>%
  select(-date, -store, -dept) %>%
  nest() %>%
  mutate(cor = future_map(data, get_correlations, .progress = TRUE))

overall_cor %>% unnest(cor) %>% select(-data) %>% arrange(desc(abs(cor)))
```


```{r}
rm(cor_df, feats, overall_cor,stores, train_w_var, tsib, wm_feats, wm_test, wm_train)
```

```{r}
trunc_svd <- function(data_matrix, components, og_data) {
  z <- svd(data_matrix, nu = components, nv = components)
  s <- diag(z$d[1:components], components, components) #Takes 6 largest components
  rec <- (z$u %*% s %*% t(z$v) %>% as.data.frame())
  proc <- rec %>% mutate(comp = components) %>% bind_cols(og_data %>% select(date, is_holiday)) %>% gather(key = store, value = rec, starts_with("V")) %>% mutate(store = str_sub(store, 2, -1) %>% as.integer)

  return(proc)
}
```


```{r}
wm_train %>%
  filter(dept %in% 1:10) %>%
  mutate(doy = yday(date)) %>%
  ggplot(aes(x = doy, y = weekly_sales, group = store)) + geom_line(alpha = .5) + facet_wrap(year(date)~dept, nrow = 3, scales = "free_y")
```


```{r}
wm_dex <- wm_train %>%
  filter(dept == 1) %>%
  spread(key = store, value = weekly_sales)

wm_svd <- wm_dex %>%
  select(-dept, -date, -is_holiday) %>%
  as.matrix()

```

```{r}
trunc_svd_var_exp <- function(train_df) {
  wm_dex <- train_df %>%
    spread(key = store, value = weekly_sales)

  wm_svd <- wm_dex %>%
    select(-date, -is_holiday) %>%
    mutate_all(replace_na, replace = 0) %>%
    as.matrix()
  
  1:ncol(wm_svd) %>% map_dfr(~trunc_svd(wm_svd, .x, wm_dex)) %>% 
    left_join(train_df %>% mutate(store = as.integer(store))) %>%
      mutate(weekly_sales = replace_na(weekly_sales, 0)) %>% 
    mutate(error = weekly_sales - rec) %>%
    group_by(comp) %>%
    summarize(
      sd_error = sd(error),
      sd_sales = sd(weekly_sales),
      pct_error = sd_error/sd_sales,
      var_exp = 1 - pct_error
    )
}

library(furrr)
plan(multiprocess)
svd_var_exp_df <- wm_train %>%
  group_by(dept) %>%
  nest()

svd_var_exp <- svd_var_exp_df %>%
  mutate(
    var_exp = future_map(data, safely(trunc_svd_var_exp), .progress = TRUE)
  ) %>%
  unnest(var_exp)


tt <- svd_var_exp %>%
  mutate(
    t = map_lgl(var_exp, is.null),
    e = map_lgl(var_exp, is.tibble),
  ) %>%
  filter(!t, e) %>%
  select(-data)

tt %>% unnest(var_exp) %>%
  ggplot(aes(x = comp, y = var_exp, group = dept)) + geom_line() + scale_y_continuous(limits = seq(0, 1)) + facet_wrap(~dept)
```

```{r}
  wm_dex <- 
svd_var_exp_df$data[[5]] %>% 
    spread(key = store, value = weekly_sales)

  wm_svd <- wm_dex %>%
    select(-date, -is_holiday) %>%
    mutate_all(replace_na, replace = 0) %>%
    as.matrix()
  
  
  svd(wm_svd, 10, 10)
  
ex <- 1:ncol(wm_svd) %>% map_dfr(~trunc_svd(wm_svd, .x, wm_dex))

ex%>% 
    left_join(svd_var_exp_df$data[[5]] %>% mutate(store = as.integer(store))) %>% 
    mutate(weekly_sales = replace_na(weekly_sales, 0)) %>% 
    mutate(error = weekly_sales - rec) %>%
    group_by(comp) %>%
    summarize(
      sd_error = sd(error),
      sd_sales = sd(weekly_sales),
      pct_error = sd_error/sd_sales,
      var_exp = 1 - pct_error
    )
```



#Rossman
```{r}
rs_path <- "~/kaggle/rossmann/"
rs_trn <- fread(file.path(rs_path, "train.csv")) %>% janitor::clean_names() %>% mutate(date = ymd(date))
rs_store <- fread(file.path(rs_path, "store.csv")) %>% janitor::clean_names()
rs_states <- fread(file.path(rs_path, "state_names.csv")) %>%
  janitor::clean_names() %>%
  left_join(fread(file.path(rs_path, "store_states.csv")) %>% janitor::clean_names())

weather <- fread(file.path(rs_path, "weather.csv"), blank.lines.skip=TRUE) %>% janitor::clean_names() %>% mutate(date = ymd(date))
```

```{r}
rs_tsib <- rs_trn %>%
  as_tsibble(key = store, index = date)

rs_feats <- rs_tsib %>%
  features(sales, list(feat_stl, feat_spectral))

rs_feats %>%
  gather(key = variable, value = value, seasonal_strength_week, trend_strength, spectral_entropy) %>%
  ggplot(aes(x = value)) + geom_histogram() + facet_wrap(~variable)
  
```




```{r}
rs_feats %>%
  mutate(ds = "Rossmann") %>% 
  select(-store) %>%
  mutate(seas_strength = seasonal_strength_week) %>%
#  bind_rows(
# wm_feats %>% mutate(ds = "Walmart") %>% select(-store) %>% mutate(seas_strength = seasonal_strength_year)
#  ) %>%
  gather(key = variable, value = value, seas_strength, trend_strength, spectral_entropy) %>%
  ggplot(aes(x = value, fill = ds)) + geom_histogram() + facet_wrap(ds~variable)
```

```{r}
rs_w_feat <- rs_trn %>%
  left_join(rs_states) %>%
  left_join(weather, by = c("state_name" = "file", "date"))

rs_cor_df <- rs_w_feat %>%
  select(-day_of_week, -state_name, -state, -max_gust_speed_km_h) %>%
  as.tibble() %>%
  group_by(store) %>%
  mutate_if(is.character, ~as.numeric(as.factor(.x))) %>%
  select(-date, weekly_sales = sales) %>%
  nest() %>%
  mutate(cor = future_map(data, get_correlations, .progress = TRUE, ))

rs_cor_df %>%
  unnest(cor) %>%
  group_by(variable) %>%
  mutate(mean_val = mean(abs(cor), na.rm = TRUE)) %>%
  ggplot(aes(x = cor)) + geom_histogram() + geom_vline(aes(xintercept = mean_val)) + facet_wrap(~variable)
```

```{r}
overall_cor <- rs_w_feat %>%
  select(-day_of_week, -state_name, -state, -max_gust_speed_km_h) %>%
  as.tibble() %>%
  mutate_if(is.character, ~as.numeric(as.factor(.x))) %>%
  select(-date, weekly_sales = sales) %>%
  get_correlations()

overall_cor %>% arrange(desc(abs(cor)))
```

```{r}
pcs <- rs_feats %>%
  select(-store) %>%
  mutate_all(~replace_na(.x, replace = mean(.x, na.rm = TRUE))) %>%
  prcomp(scale=TRUE)


autoplot(pcs, loadings = TRUE, loadings.label = TRUE)

```


```{r}
rm(rs_trn, rs_tsib, rs_w_feat, weather, rs_store, rs_states, rs_feats, rs_cor_df)
```


# Grupo Bimbo
```{r}
bb_trn <- fread("~/kaggle/bimbok/train.csv") %>% janitor::clean_names()
bb_test <- fread("~/kaggle/bimbok/test.csv") %>% janitor::clean_names()

bb_fact <- bb_trn[, lapply(.SD, as.factor), .SDcols = c("agencia_id", "canal_id", "ruta_sak", "cliente_id", "producto_id")]
bb_test_fact <- bb_test[, lapply(.SD, as.factor), .SDcols = c("agencia_id", "canal_id", "ruta_sak", "cliente_id", "producto_id")]
```

```{r}
bb_trn[, .(count = .N), by = .(agencia_id, canal_id, ruta_sak, cliente_id, producto_id)] %>%
ggplot(aes(x = count)) + geom_histogram()
```

```{r}
bb_trn[, .(count = .N), by = .(agencia_id, cliente_id, producto_id, semana)] %>%
  .[, .(count = .N), by = .(agencia_id, cliente_id, producto_id)] %>%
  ggplot(aes(x = count)) + geom_histogram()
```

```{r}
bb_trn[, .(count = .N), by = .(cliente_id, producto_id, semana)] %>%
  .[, .(count = .N), by = .(cliente_id, producto_id)] %>%
  ggplot(aes(x = count)) + geom_histogram()
```


```{r}
bb_trn[, .(count = .N), by = .(agencia_id, cliente_id, producto_id, semana)] %>%
  ggplot(aes(x = count)) + geom_histogram()
```

```{r}
bb_test[, .(count = .N), by = .(agencia_id, cliente_id, producto_id, semana)] %>%
  .[order(count, decreasing = TRUE)] %>%
  ggplot(aes(x = count)) +
  stat_ecdf()

```

```{r}
bb_test[, .(count = .N), by = .(cliente_id, producto_id, semana)] %>%
  .[order(count, decreasing = TRUE)] %>%
  ggplot(aes(x = count)) +
  stat_ecdf()

```


```{r}
bb_fact[, lapply(.SD, nlevels), .SDcols = c("cliente_id", "producto_id", "agencia_id", "ruta_sak", "canal_id")]
bb_test_fact[, lapply(.SD, nlevels), .SDcols = c("cliente_id", "producto_id", "agencia_id", "ruta_sak", "canal_id")]
```

```{r}
agg <- bb_trn[, lapply(.SD, sum), .SDcols = c("venta_uni_hoy", "venta_hoy", "dev_uni_proxima", "dev_proxima", "demanda_uni_equil"), by = .(cliente_id, producto_id, semana)]
agg[order(cliente_id, producto_id, semana)]
```


```{r}
obs <- agg[, .(cliente_id, producto_id, semana, exists = 1L)]

bt <- dcast(obs, cliente_id + producto_id ~ semana, value.var = "exists") %>%
  melt(., id.vars = c("cliente_id", "producto_id"), measure.vars =  c("3", "4", "5", "6", "7", "8", "9"), variable.name = "semana")

full_ds <- merge(bt[, semana := as.integer(semana)], agg, all.x = TRUE)

rm(bt, agg)
gc()

for (j in seq_len(ncol(full_ds))) {
  set(full_ds, which(is.na(full_ds[[j]])),j,0)
}  

rm(bb_fact, bb_test_fact)

full_ds <- full_ds[order(cliente_id, producto_id, semana)]
```


```{r}
safe_spec <- possibly(feat_spectral, otherwise = NA)
full_ds[, .(entropy = safe_spec(demanda_uni_equil) %>% as.numeric), by = .(cliente_id, producto_id)]

plan(multiprocess)
full_ds_feat <- full_ds[, .(.(demanda_uni_equil)), by = .(cliente_id, producto_id)]
full_ds_feat[, ent := future_map(V1, ~safe_spec(.x) %>% as.numeric)]

```

